Startseite Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
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Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq

  • Zainab Ali Omran ORCID logo EMAIL logo und Nassrin Jassim Hussien Al-mansori ORCID logo
Veröffentlicht/Copyright: 12. Februar 2024
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Abstract

The objective of this study was to predict the suspended sediment load at upstream of the Al Kufa barrage. Ten cross-sections in the upstream region have been identified to collect field measurements. As part of the inquiry, the fluid and hydraulic parameters were measured in the field and sediment samples were collected in sections to develop an empirical formula. These variables included the gravitational constant (g), hydraulic radius (R h), mean velocity (V), median particle size (d 50), river top breadth (B), kinematic viscosity (v), and (ρ) water density. The formula was created using both statistical analysis and dimensional analysis techniques. The results showed that the newly developed formula agreed well between the calculated and observed suspended sediment discharges based on the determination coefficient R 2 of 0.979. There was a very high acceptance between the average sediment discharge from the new formula, equal to 7.365 kg/s, and the average sediment discharge from field measurements (7.142 kg/s).

1 Introduction

The sediment transport process in rivers is one of the important matters to be studied because of the morphological changes which it causes and the significant economic impacts that follow. Therefore, this study included the field study of sediment movement and water flow for the selected study area located at upstream of Al-Kufa barrage.

Decision-makers are constantly concerned that erosion, transport, and sedimentation processes may impact the majority of critical projects on or very close to riverbanks in the study region. As a result, we must increase our understanding of the appropriate equations that can be used to obtain results that are satisfactory for tracking and predicting changes that will occur in the areas of the riverbanks by monitoring the processes of erosion, sedimentation, and transport that take place in river routes [1,2,3,4,5].

In the literature, there are numerous methods for calculating sediment movement rates. However, no single equation can forecast the transfer of silt at all locations (estuaries, rivers, etc.). Many formulas and equations have been developed to calculate sediment transport discharge; however, these equations were designed taking into account the characteristics of the environment, the topography of the site, and the particular hydraulic conditions [6,7,8,9]. Yang [10] used dimensional analysis (higher than 100 ppm by weight) to modify rivers with high concentrations of suspended solids. Al-Kizwini et al. [11], with the assistance of MS-statistic, investigated the movement of sediment and advanced a novel principle to determine the amount of sediment in the Kirkuk irrigation channel using regression analysis and the technique of dimensional analysis of the research area variables. The formula was developed for 14 cross-sections, and the data were collected by choosing 24 cross-sections along the length of the channel. The results of the proposed formula were validated by comparing them to the data from the other sections and showed good agreement with the field measurements. Khassaf and Addab [12] studied how sediments were transported and how much was present in the Al-Meshkab regulator channel. Twenty-four cross-sections were chosen throughout the Euphrates River reach to investigate the characteristics and pace of sediment transport. The practical portion of the study (laboratory and field activities) and the statistical part were separated into two sections. The study included the proposition of an experimental principle that has been applied to appropriate the dimensionless formula and forecast the link between the sediment rate and the various factors. The disparities between the observed and projected sediment rates were investigated using an analysis of variance. The findings showed that the statistical model could not detect any statistically significant discrepancies between the observed and estimated sediment rate values. Alak [13] investigated how sediment has been transported upstream of the Al-Amarah barrage. Flow velocity measurements were taken in the field, river cross-sections were drawn using the acoustic Doppler current profiler (ADCP), and comparisons were made using a working model and the three-dimensional numerical model sediment simulation in intakes with multiblok option (SSIIM). The goal of this study was to see whether the numerical model (SSIIM) could accurately predict the sediment allocation in the study by comparing model results to those observed in the field. A new sediment transport formula and the suggestion and verifying of soil infiltration rating curves were also presented. Mei et al. [14] proposed a mountain–river–desert conjecture, which is a green grass formed in the area fronting the river and with the mountains at the back; however, on the opposite the river, the area has an inherent trend to be desertified. This discovery has opened the path for a new way to reverse the desert in the future and found the missing piece of the puzzle for the land’s desertification, and it has changed everything you ever thought for the desert.

Mei et al. [15], when the Yellow River flows through the Hetao Plain in Inner Mongolia, north China, it forms a mountain-river-desert system with the Yin Mountains on one side and the Hobq desert on the other side. In this mountain–river–desert system, the mountains are in the back and the land in front of the river develops a dense growth of green grass; however, the area on the other side of the river has a natural propensity to lose moisture, which may lead to desertification. This study analyzes the Yellow River bed shift from 2014 to 2021 to confirm this theoretical hypothesis. The seven-year shift revealed that the river is the natural border between grassland and desert, and it had nothing to do with the former earth surfaces of the river banks.

This study aims to conceptualize the region of study and develop a new formula for evaluating suspended sediment load at upstream of Al-Kufa Barrage, Kufa City, Iraq, using dimensional analysis with the Alghazali formula and verifying the outcomes of the new method.

2 Materials and methods

2.1 Area of the study

Al-Kufa barrage has been built for irrigation purposes on the Kifil-Shanafiyah branch of the Euphrates River downstream of the Babylon Governorate since 1986. In the middle Euphrates region, the barrage controls the flow for the upstream regulator. The barrage is designed to discharge 1,400 m3/s of flood water, with an upstream water level of 25.7 m above sea level [16].

2.2 Field data

Ten transect sections used in this study are depicted in Figure 1. The sections were chosen precisely and according to the morphology of the area, and the number was limited to ten sections. The distance between one section and another is approximately 200 m. Note that the sections are clearly marked on the map, which shows their number and location correctly and according to the coordinates fixed on the study land. So specialists were hired to map the sections and used geographic information system to install them correctly. The following were among the field measures that involved gathering the information needed to accomplish the study’s goals:

  1. Determine the locations of the cross-sections and their coordinates, and then, determine each section’s geometrical characteristics such as the cross-section’s area, top width, and hydraulic radius.

  2. Determine flow velocities and water discharge for all cross-sections using the ADCP technique.

  3. Take samples from the river bed and for all cross-sections using the Van Veen grab device.

  4. Take samples of the suspended sediment load using a suspended sampler device for all cross-sections.

Figure 1 
                  Locations of cross-sections in the study area, as determined using the SAS algorithm.
Figure 1

Locations of cross-sections in the study area, as determined using the SAS algorithm.

2.3 Hydraulic measurements

Using ADCP technology, top width, cross-sectional area, discharge, water velocity, and water level were all measured for each cross section. The field measurements were taken from June to September during the season because this period with the most possible changes in sediment amounts is the period of low water levels. Especially, since the summer period in the region is much greater than the winter period and rainfall. Version 5.5a of the software and ADCP were used (Figure 2).

Figure 2 
                  Cross-section No. 1 from Al-Kufa barrage.
Figure 2

Cross-section No. 1 from Al-Kufa barrage.

2.4 Suspended sediment concentration

The amount of suspended silt in each segment varies dramatically over time. The desired level of measurement precision should be considered when deciding how many and where to position sample verticals. Various methods can be used to sample sediment suspended in streams. The quantity and locations of the necessary vertical samples were chosen according to the Inter-agency Committee on Water Resources’ suggestions [7]. The flow’s cross-section was split into three pieces, each with an almost equal discharge at each transect. Sample verticals were selected at 1/4, 1/2, and 3/4 of the stream flow breadth. At depths of 0.8, 0.6, and 0.2 D, D represents the separation from the water’s surface; three samples were taken [17].

2.5 Bed materials

To learn more about the bed’s makeup, sediment samples were taken using the Van Veen Grab instrument, and three bed material samples were collected for the study’s various sections. The Van Veen grab is crucial equipment for sampling the bed surface. It takes samples from the bottom of estuaries, rivers, lakes, etc. since it is made of stainless steel.

2.6 Laboratory works

From each cross-section, the samples were obtained for sieve analysis and a hydrometer test. In a laboratory, the bed material’s sediment concentration and particle size distribution curve must be produced.

The following equation is used to compute the concentration of the suspended sediment [13]:

(1) C = W 2 W 1 V ,

where C is the suspended sediment concentration, (ppm), W 2 is the dry filter paper + suspended sediment weight (mg), and V is the water volume (l).

American society for testing and materials (ASTM) D854-2 showed a specific gravity test price of 2.66. The gradation curve was used to study the distribution of grain sizes. In accordance with ASTM D422 [18], the test was conducted in two stages: for sands and gravels, sieve analysis is utilized, and for fine-grained soils (silts and clays), hydrometer analysis was employed (Figure 3).

Figure 3 
                  Sieve analysis for all sections.
Figure 3

Sieve analysis for all sections.

3 Results and discussions

3.1 Development of empirical formula

Dimensional analysis is helpful to convert physical quantities into dimensionless quantities [19].

Numerous formulaes and equations have been developed for estimating river sediment transport rates (discharge). However, no single equation can accurately predict the sediment transport for rivers and all locations. In this work, a new dimensional analysis method proposed by Alghazali is applied, which can be expressed as follows [20]:

Q s = f ( ρ , ρ s , d 50 , ν , V , B , g , R h ) .

As shown in Table 1 below,

Table 1

Repeating and nonrepeating variable matrices

R 1 R 2 R 3 U 1 U 2 U 3 U 4 U 5 U 6
d 50 ρ s g Q s ρ ν V B R h
L 1 −3 1 0 −3 2 1 1 1
M 0 1 0 1 1 0 0 0 0
T 0 0 −2 −1 0 −1 −1 0 0

The repeated variable matrix must be changed into an identity (unit) matrix for the method to work. Below are the instructions and tables for how to do this:

  • Multiplying row number 2 by three and adding it to row number 1, then dividing row number 3 by (−2)

R 1 R 2 R 3 U 1 U 2 U 3 U 4 U 5 U 6
d 50 ρ s g Q s ρ ν V B R h
L 1 0 1 3 0 1 1 1 1
M 0 1 0 1 1 0 0 0 0
T 0 0 1 0.5 0 0.5 0.5 0 0
  • Row 3 is subtracted from row 1.

R 1 R 2 R 3 U 1 U 2 U 3 U 4 U 5 U 6
d 50 ρ s g Q s ρ ν V B R h
L 1 0 0 2.5 0 1.5 0.5 1 1
M 0 1 0 1 1 0 0 0 0
T 0 0 1 0.5 0 0.5 0.5 0 0

Therefore, the dimensional equation will be as follows:

(2) Q s = 9.36 × 10 19 ρ s 2.57 V 1.2 B 1.38 R h 1.18 d 50 1.5 ν 36 .

By contrasting the estimated values with the observed values, it was possible to determine whether the empirical equation could be used to estimate the suspended sediment transport rate in the Al Kufa barrage. Figure 4 shows the computed and measured values of sediment load in a generally recognized relationship with an R² correlation value of 0.979.

Figure 4 
                  Observed and computed suspended sediment load on a scatter plot.
Figure 4

Observed and computed suspended sediment load on a scatter plot.

3.2 Formula verification

  • The proposed formula’s verification using graphical method

It is necessary to compare the calculated outcomes to the measured data to validate the suggested formula’s effectiveness. The first seven parts of the estimated sediment discharge were verified using the proposed procedure.

Figure 5 shows the new formula verification. With R² = 0.98, the observed and computed sediment transport rates showed a reasonable agreement. According to the findings, the suggested formula yields an average total suspended sediment load of 7.4 ppm.

  • Verification of proposed formula using statistical relations

Figure 5 
                  Verification plot for graphical method.
Figure 5

Verification plot for graphical method.

By comparing the suggested formula with the observed values using two statistical methods that were used to confirm the proposed procedure, the viability of the proposed formula for estimating the suspended sediment discharge was assessed.

3.2.1 Mean standard error (MSE)

The degree of convergence between the estimated and measured values produced by the suggested formula was evaluated using the method, where the statistical criteria were near zero [21,22,23]. It can be computed from the following equation, exhibiting great accuracy in the suggested equation’s results:

(3) MSE = 100 N n = 1 n S o S c S o ,

where MSE is the mean standard error, S o is the sediment discharge observed (kg/s), S C is the calculated sediment discharge (kg/s), and N is the number of the predicted values.

3.2.2 Root mean squared error (RMSE)

Error analysis uses the RMSE technique. It determines how far an estimated (observed or calculated) value deviates from the required actual weight [24]. Observed and estimated values should agree if the RMSE values are relatively modest [25]. It can be calculated using the following equation:

(4) RMSE = i = 1 n ( S o S c ) 2 N ,

where S o is the observed sediment discharge (kg/s), S c is the calculated sediment discharge (kg/s), and N is the number of observed values.

Table 2 shows the values of the statistical parameters between the calculated and observed suspended sediment discharge for the seven cross-sections. Through the importance of the estimated statistical parameters, it is evident that the prediction using the proposed formula for the values of suspended sediment discharge is good compared to the observed values. The proposed formula s verification using Statistics, like Mean standard error (MSE), which reached MSE = 4.5% and RMSE = 0.377. A very high acceptance between the average sediment discharge from the new formula and average sediment discharge from field measurements.

Table 2

Comparison using statistical parameters

Percentage of data in the range Number of data sets
MSE 4.5% 7
RMSE 0.377 7

4 Conclusions

This study developed a new sediment transport formula based on the Alghazali formula and dimensional analysis. The empirical equation’s applicability was evaluated by equating the computed values with the actual values for ten sections. According to the results obtained in this study, the following points were concluded:

A good agreement was observed between the measured and computed values of the total sediment discharge for the statistical model, depending on the determination coefficient, R² = 0.979.

A different set of measured data was used for the verification process (seven sections). The two verification methods agreed well with the estimated and observed sediment transport rates.

  1. Funding information: Also, I declare that the manuscript was done depending on the personal effort of the author, and there is no funding effort from any side or organization.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: Most datasets generated and analyzed in this study are in this submitted manuscript. The other datasets are available on a reasonable request from the corresponding author with the attached information.

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Received: 2023-09-23
Revised: 2023-10-09
Accepted: 2023-10-14
Published Online: 2024-02-12

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
Heruntergeladen am 4.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/eng-2022-0541/html
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